CT reconstruction quality control

ABSTRACT

A computer-implemented method, system, and instructions can include receiving a reconstructed volume of an object; determining an air density and a material density from the reconstructed volume of the object; subdividing the reconstructed volume into one or more blocks, each block comprising one or more voxels; determining one or more contrast blocks within the reconstructed volume; and determining a reconstruction quality based on the number of contrast blocks in the reconstructed volume.

BACKGROUND

A computed tomography scan (“CT scan”) typically involves placing aphysical object on a rotating platform inside a Computed Tomographyscanner (CT scanner) between an x-ray source and x-ray detector androtating the object around an axis of rotation to generate radiographsfrom the x-rays detected by the detector. Conventionally, the CT scannercan tomographically reconstruct the radiographs into a 3D representationof the object scanned (“CT reconstruction”). One example of CTreconstruction can be found in, for example, in the publicationPrinciples of Computerized Tomographic Imaging (A. C. Kak and MalcolmSlaney, Principles of Computerized Tomographic Imaging, IEEE Press,1988), the entirety of which is incorporated by reference herein. Othertypes of CT reconstruction can also be performed.

CT scanners are typically configured with calibration parameters whichare provided to reconstruction algorithms to generate the reconstructedimage. However, over time, CT scanners can be subject to factors thatcan alter physical component alignment and relationships between them.These factors can render the initial parameters ineffective toreconstruct an accurate or clear image. Accordingly, CT scanners canneed calibration. However, it can be difficult and problematic torecognize when CT scanners need calibration in general, and also inautomated systems where user oversight/interaction during the CTscanning process is limited.

SUMMARY

Disclosed is a computer-implemented method of reconstruction qualitycontrol that can include: receiving a reconstructed volume of an object;determining an air density and a material density from the reconstructedvolume of the object; subdividing the reconstructed volume into one ormore blocks, each block comprising one or more voxels; determining oneor more contrast blocks within the reconstructed volume; and determininga reconstruction quality based on the number of contrast blocks in thereconstructed volume.

Disclosed is a system of reconstruction quality control that caninclude: a processor; a computer-readable storage medium comprisinginstructions executable by the processor to perform steps that caninclude: receiving a reconstructed volume of an object; determining anair density and a material density from the reconstructed volume of theobject; subdividing the reconstructed volume into one or more blocks,each block comprising one or more voxels; determining one or morecontrast blocks within the reconstructed volume; and determining areconstruction quality based on the number of contrast blocks in thereconstructed volume.

Disclosed is a non-transitory computer readable medium storingexecutable computer program instructions for reconstruction qualitycontrol, the computer program instructions comprising instructions for:receiving a reconstructed volume of an object; determining an airdensity and a material density from the reconstructed volume of theobject; subdividing the reconstructed volume into one or more blocks,each block comprising one or more voxels; determining one or morecontrast blocks within the reconstructed volume; and determining areconstruction quality based on the number of contrast blocks in thereconstructed volume.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a schematic diagram of a computed tomography (CT) scanningsystem.

FIG. 2 shows a 2-dimensional (2D) radiographic image of a dentalimpression tray containing a dental impression.

FIG. 3 shows a cross-section of a 3-dimensional (3D) volumetric image.

FIGS. 4(a)-4(b) are histograms illustrating a density frequencydistribution.

FIG. 5 shows a perspective view of an illustration of an example 3Dreconstructed image in some embodiments.

FIGS. 6(a)-6(c) show perspective views of illustrations of examples of asingle block in 3D.

FIG. 7 shows an example of a slice of a reconstructed image showingregions of high contrast.

FIG. 8 shows an example of a slice of a reconstructed image that isblurry due to below threshold value number of contrast blocks.

FIG. 9 shows an illustration of an example of one type of visual alertin some embodiments.

FIG. 10 shows a flow chart of a method in some embodiments.

FIG. 11 shows a diagram of a system in some embodiments.

DETAILED DESCRIPTION

For purposes of this description, certain aspects, advantages, and novelfeatures of the embodiments of this disclosure are described herein. Thedisclosed methods, apparatus, and systems should not be construed asbeing limiting in any way. Instead, the present disclosure is directedtoward all novel and nonobvious features and aspects of the variousdisclosed embodiments, alone and in various combinations andsub-combinations with one another. The methods, apparatus, and systemsare not limited to any specific aspect or feature or combinationthereof, nor do the disclosed embodiments require that any one or morespecific advantages be present or problems be solved.

Although the operations of some of the disclosed embodiments aredescribed in a particular, sequential order for convenient presentation,it should be understood that this manner of description encompassesrearrangement, unless a particular ordering is required by specificlanguage set forth below. For example, operations described sequentiallymay in some cases be rearranged or performed concurrently. Moreover, forthe sake of simplicity, the attached figures may not show the variousways in which the disclosed methods can be used in conjunction withother methods. Additionally, the description sometimes uses terms like“provide” or “achieve” to describe the disclosed methods. The actualoperations that correspond to these terms may vary depending on theparticular implementation and are readily discernible by one of ordinaryskill in the art.

As used in this application and in the claims, the singular forms “a,”“an,” and “the” include the plural forms unless the context clearlydictates otherwise. Additionally, the term “includes” means “caninclude.” Further, the terms “coupled” and “associated” generally meanelectrically, electromagnetically, and/or physically (e.g., mechanicallyor chemically) coupled or linked and does not exclude the presence ofintermediate elements between the coupled or associated items absentspecific contrary language.

In some examples, values, procedures, or apparatus may be referred to as“lowest,” “best,” “minimum,” or the like. It will be appreciated thatsuch descriptions are intended to indicate that a selection among manyalternatives can be made, and such selections need not be better,smaller, or otherwise preferable to other selections.

In the following description, certain terms may be used such as “up,”“down,” “upper,” “lower,” “horizontal,” “vertical,” “left,” “right,” andthe like. These terms are used, where applicable, to provide someclarity of description when dealing with relative relationships. But,these terms are not intended to imply absolute relationships, positions,and/or orientations. For example, with respect to an object, an “upper”surface can become a “lower” surface simply by turning the object over.Nevertheless, it is still the same object.

Some embodiments of the present disclosure can include acomputer-implemented method of reconstruction quality control. Someembodiments of the computer-implemented method can include receiving areconstructed volume of an object. In some embodiments, thereconstructed volume is a CT reconstructed volume.

A computed tomography (CT) scanner uses x-rays to make a detailed imageof an object. A plurality of such images are then combined to form a 3Dmodel of the object. A schematic diagram of an example of a CT scanningsystem 140 is shown in FIG. 1 . The CT scanning system 140 includes asource of x-ray radiation 142 that emits an x-ray beam 144. An object146 being scanned is placed between the source 142 and an x-ray detector148. In some embodiments, the object can be any object that can, forexample, fit in a CT scanning system and be penetrated by x-rays. Thex-ray detector 148, in turn, is connected to a processor 150 that isconfigured to receive the information from the detector 148 and toconvert the information into a digital image file. Those skilled in theart will recognize that the processor 150 may comprise one or morecomputers that may be directly connected to the detector, wirelesslyconnected, connected via a network, or otherwise in direct or indirectcommunication with the detector 148.

An example of a suitable scanning system 140 includes a Nikon Model XTH255 CT Scanner (Metrology) which is commercially available from NikonCorporation. The example scanning system includes a 225 kV microfocusx-ray source with a 3 μm focal spot size to provide high performanceimage acquisition and volume processing. The processor 150 may include astorage medium that is configured with instructions to manage the datacollected by the scanning system. A particular scanning system isdescribed for illustrative purposes; any type/brand of CT scanningsystem can be utilized.

One example of CT scanning is described in U.S. Patent Application No.US20180132982A1 to Nikolskiy et al., which is hereby incorporated in itsentirety by reference. As noted above, during operation of the scanningsystem 140, the object 146 is located between the x-ray source 142 andthe x-ray detector 148. A series of images of the object 146 arecollected by the processor 150 as the object 146 is rotated in placebetween the source 142 and the detector 146. An example of a singleradiograph 160 is shown in FIG. 2 . The radiograph 160 and allradiographs described herein are understood to be digital. In oneembodiment, a series of 720 images can be collected as the object 146 isrotated in place between the source 142 and the detector 148. In otherembodiments, more images or fewer images may be collected as will beunderstood by those skilled in the art. In some embodiments, radiographscan be referred to as projection images.

The plurality of radiographs 160 of the object 146 are generated by andstored within a storage medium contained within the processor 150 of thescanning system 140, where they may be used by software contained withinthe processor to perform additional operations. For example, in anembodiment, the plurality of radiographs 160 can undergo tomographicreconstruction in order to generate a 3D virtual image 170 (see FIG. 3 )from the plurality of 2D radiographs 160 generated by the scanningsystem 140. In the embodiment shown in FIG. 3 , the 3D virtual image 170is in the form of a volumetric image or volumetric density file (shownin cross-section in FIG. 3 ) that is generated from the plurality ofradiographs 160 by way of a CT reconstruction algorithm associated withthe scanning system 140. One type of CT reconstruction algorithm can bethe filtered backprojection algorithm as described in the Principles ofComputerized Tomographic Imaging publication. Other types of CTreconstruction algorithms known in the art can also be used.

A reconstructed image of a scanned object can include one or more objectmaterial voxels, air voxels, and other material voxels. As discussedpreviously, incorrect CT scanner calibration parameters can distort thereconstructed volume. One example of CT scanner calibration parameterscan be found in U.S. Utility patent application Ser. No. 16/887,437 ofNikolskiy et al., filed May 29, 2020, the entirety of which is herebyincorporated by reference. A distorted reconstructed volume can occur,for example, because in some cases, the reconstruction algorithmgenerates a disproportionate number of non-air and non-object voxels inthe reconstructed volumetric image, thereby distorting or blurring thereconstructed volumetric image. In some embodiments, the number ofnon-air and non-object material voxels in distorted or blurredreconstructed images can be greater than in undistorted or goodreconstructed images. In some cases, the distorted/blurry reconstructedimage can be caused by incorrect CT scanner calibration parameters thatare used in the reconstruction algorithm. In some cases, blurryreconstructed volumes can also occur where an object is not properlyfixed to the rotating platform and therefore moves unexpectedly duringCT scanning.

In some embodiments, the object can be any object that can fit within aCT scanning system, for example. In some embodiments, the object can beany object used in the field of medicine and/or dentistry. In someembodiments, the object can be a physical dental impression, forexample. In some embodiments, the dental impression can include atriple-tray impression. In some embodiments, the dental impression caninclude an arch impression.

In some embodiments, the computer-implemented method can determine anair density and a material density of the object from the reconstructedvolume. In some embodiments, the material density can be of the objectscanned. In some embodiments, the material density can include a dentalimpression material's density. In some embodiments, the air and/or thematerial density is set by a user in a configuration file. In someembodiments, the air and/or material density is determined automaticallyfrom a histogram.

In some embodiments, the histogram can provide a frequency distributionof material densities in a scan volume. In some embodiments, thevolumetric file can be received by the computer-implemented system. Thevolumetric density file contains voxels having density information ofone or more materials and surrounding air in a CT scan volume. Thenumber of voxels at a particular density value can represent the amountof the material/air having that particular density.

As illustrated in the histogram 211 of FIG. 4(a), in some embodiments,the computer-implemented method can generate a density frequencydistribution of the volumetric density file. The histogram 211 is shownfor illustrative purposes and includes an x-axis 213 of density valuesand the y-axis 214 of the number of voxels (voxel counts), for example.All histograms illustrating the density frequency distribution hereininclude an x-axis of density values and a y-axis of the number of voxels(voxel counts). The computer-implemented method can receive a volumetricdensity file. The computer-implemented method can generate a normalizedscan density range 201 for the volumetric density file. For example, insome embodiments, the computer-implemented method can generate thenormalized scan density range 201 to be between 0.0 and 1.0. Thecomputer-implemented method can subdivide the normalized scan densityrange into one or more scan density subranges 203 (scan density subrange203 is shown among multiple scan density subranges in a magnified view).For example, the computer-implemented method can subdivide thenormalized scan density range 201 of 0.0 and 1.0 into multiple scandensity subranges 203. In some embodiments, the number of scan densitysubranges 203 can be 500, for example. In some embodiments, more orfewer scan density subranges 203 are possible. For each voxel, thecomputer-implemented method normalizes the density value of the voxel tofall within the normalized scan density range 201. Thecomputer-implemented method compares the normalized density of the voxelwith the one or more scan density subranges 203 and increments the voxelcount for the scan density subrange 203 within which the normalizedvoxel density value falls. The computer-implemented method loads thenext voxel from the volumetric density file and repeats the process forevery voxel in the volumetric density file to determine the total voxelcount for each of the scan density subranges 203. In some embodiments,the computer-implemented method takes a logarithm of the voxel countsfor each scan density subrange 203. The computer-implemented method inthis manner generates the density frequency distribution which isdepicted as histogram 211 for illustrative purposes.

In some scans, air occupies most of the CT scan volume. This can occur,for example, in CT scans where the object being scanned occupies asmaller volume of the CT scan than air. In the case of dentalimpressions, for example, this can include triple tray impressions orother dental impressions whose impression material occupies a smallervolume of the CT scan volume than air. In such scans, since air has thehighest volume, the number of voxels with a density value falling withinthe density range of air is highest. In some embodiments, the number ofvoxels with a density value falling within the density range of theobject or the impression material in the case of dental impressions canbe the lowest. In some embodiments, the dental impression can include ahandle, which occupies the least volume, so that the impression materialoccupies the second highest volume in the CT scan volume next to air.The number of voxels having a density falling within the density rangeof the impression material can therefore be the second highest.Similarly, other materials such as the handle can constitute the leastamount of material and therefore occupy the least volume in the CT scanvolume. The number of voxels having a density falling within the densityrange of the handle material can therefore have the lowest voxel count.

In another example, the particular object being scanned or the dentalimpression material can occupy most of the CT scan volume. In the caseof dental impressions, this can occur in CT scans of full archimpressions, for example, or other dental impressions whose impressionmaterial occupies more of the CT scan volume than air. In such scans,the impression material of the dental impression can occupy the mostvolume in the CT scan volume. The number of voxels having a densityvalue falling within the density range of the object or the impressionmaterial can therefore be the highest voxel count. The number of voxelshaving a density value falling within the density range of air cantherefore be the lowest voxel count. In case of a dental impression thatincludes a handle, the air in such a scan can occupy the second highestvolume in the CT scan volume. The number of voxels having a densityfalling within the density range of air can therefore be the secondhighest voxel count. Similarly, other materials such as the handle canconstitute the least amount of material in the CT scan so that thenumber of voxels having a density falling within the density range ofthe handle material can have the lowest voxel count.

In some embodiments, the computer-implemented method is provided thetype of object scanned. This can include, for example, whether theobject being scanned occupies the most volume or air occupies the mostvolume. In the case of dental impressions, the value can be determinedfrom the type of scan performed. For example, if a triple-tray dentalimpression scan was performed, the computer-implemented method candetermine that air will occupy the highest scan volume (and thereforethe most voxels), the impression material will occupy the second highestvolume (and therefore the second most number of voxels), and that thehandle will occupy the least volume (and therefore the least number ofvoxels) in some embodiments. For example, if a full arch dentalimpression scan was performed, the computer-implemented method candetermine that the impression material will occupy the highest scanvolume (and therefore the most voxels), air will occupy the secondhighest volume (and therefore the second most number of voxels), andthat the handle will occupy the least volume (and therefore the leastnumber of voxels) in some embodiments.

In some embodiments, the computer-implemented method can determine theair density and the object material density (such as, for example,impression material density) based on voxel counts in the densityfrequency distribution. In some embodiments, the computer-implementedmethod can use voxel counts to determine one or more voxel count peaksas the highest voxel counts in the density frequency distribution. Forexample, the computer-implemented method can compare the voxel counts ateach scan density subrange and determine which scan density subrange thevoxel count either switches from increasing to decreasing, or beginsdecreasing. In some embodiments, a voxel count peak can span one or morescan density subranges. Other techniques can be used to determine voxelcount peaks in the density frequency distribution. In some embodiments,a voxel count peak can span one or more scan density subranges. In someembodiments, the number of peaks in the density frequency distributionis proportional to the number of materials plus air in the CT scanvolume, for example. In some embodiments, the valleys are arrangedbetween two voxel count peaks.

For example, as illustrated in FIG. 4(a), the computer-implementedmethod can generate a density frequency distribution which isillustrated in the histogram 211. The computer-implemented method candetermine a highest voxel count peak 216, second highest voxel countpeak 220, and third voxel count peak 218, for example. Additional voxelcount peaks can also be present and determined by thecomputer-implemented method. As discussed previously, highest voxelcount peak 216, second highest voxel count peak 220, third voxel countpeak 218 and any other peaks herein can span one or more scan densitysubrange(s). The peaks can be separated by a first valley 230 betweenthe highest voxel count peak 216 and the second highest voxel count peak220, and a second valley 233 between the second highest voxel count peak220 and the third highest voxel count peak 218. The valleys can define ahighest peak density range 231, a second highest peak density range 234,and a lowest peak density range 232, for example.

The computer-implemented method receives a volumetric density file andgenerates a density frequency distribution. The computer-implementedmethod receives information regarding a type of impression or objectscanned. The computer-implemented method determines whether air or theobject material (or impression material) occupies the most volume of theCT scan volume based on the type of impression or object scanned. Insome embodiments, the computer-implemented method determines airoccupies the most volume if the impression type is a triple trayimpression, for example. In some embodiments, the computer-implementedmethod determines the object material (impression material) occupies themost volume if the impression type is a full arch impression, forexample. The computer-implemented method determines voxel count peaks inthe density frequency distribution. If the computer-implemented methoddetermines that air occupies most of the CT scan volume and the objectmaterial (or impression material) occupies the second highest volumebased on the type of impression scanned, the computer-implemented methoddetermines the air density as the one or more density subranges of thehighest voxel count peak and the object material (or impressionmaterial) density as the one or more density subranges of second highestvoxel count peak. If the computer-implemented method determines that anobject material (or impression material) occupies most of the CT scanvolume and air occupies the second most based on the type of impressionscanned, the computer-implemented method determines the object material(or impression material) density as the one or more density subranges ofthe highest voxel count peak and the air density as the one or moredensity subranges of the second highest peak. In some embodiments, theexample of FIG. 4(a), air occupies the most CT scan volume. Thecomputer-implemented method can determine that the highest peak densityrange 231 is the air density and the second highest peak density range234 is the object material (or impression material) density. FIG. 4(b)illustrates another example in which the other material such as thehandle, for example, has a greater density than the object material (orimpression material). In FIG. 4(b), air occupies the most CT scanvolume. The computer-implemented method generates a density frequencydistribution as represented by histogram 222 for illustrative purposes.The density frequency distribution can include a highest voxel countpeak 244, a second highest voxel count peak 246, and a third highestvoxel count peak 248. The computer implemented-method determines thatair occupies the most CT scan volume. The computer-implemented methoddetermines the air density as the highest voxel count peak 244 and theobject material (or impression material) density as the second highestvoxel count peak 246. In some embodiments, total voxel counts in thedensity ranges can be used instead of voxel count peaks to determine theair density and the object material (or impression material) density.For example, in some embodiments, the computer-implemented methodreceives a volumetric density file and generates a density frequencydistribution. The computer-implemented method receives informationregarding a type of impression scanned. The computer-implemented methoddetermines whether air or the object material (or impression material)occupies the most volume of the CT scan volume based on the type ofimpression scanned. In some embodiments, the computer-implementeddetermines air occupies the most volume if the impression type is atriple tray impression. In some embodiments, the computer-implementeddetermines the object material (or impression material) occupies themost volume if the impression type is a full arch impression. Thecomputer-implemented method determines voxel count peaks in the densityfrequency distribution. The computer-implemented method determines oneor more voxel count valleys between the voxel count peaks. Thecomputer-implemented method determines one or more density rangesbetween the valleys, between 0.0 or the minimum density value of thenormalized density range and a first valley, and between the last valleyand the maximum density value of the normalized density range. Thecomputer-implemented method calculates a density range voxel count foreach of the one or more density ranges. In some embodiments, thecomputer-implemented method can count the total number of voxels in eachof the one or more density ranges. In some embodiments, thecomputer-implemented method can perform an integration on a curve thatconnects the voxel count peaks within each of the one or more densityranges. If the computer-implemented method determines air occupies mostof the CT scan volume and the object material (or impression material)occupies the second highest volume based on the type of impressionscanned, the computer-implemented method determines the air density asthe highest voxel count density range and the object material (orimpression material) density as the second highest voxel count densityrange. If the computer-implemented method determines that an objectmaterial (or impression material) occupies most of the CT scan volumeand air occupies the second most based on the type of impressionscanned, the computer-implemented method determines the air density asthe second highest voxel count density range and the object material (orimpression material) density as the highest voxel count density range.For example, the computer-implemented method generates a densityfrequency distribution illustrated as histogram 211 in FIG. 4(a) asdescribed previously. As shown in FIG. 4(a), the computer-implementedmethod determines voxel count peaks 216, 218, and 220 and valleys 230and 233, thereby establishing density ranges between density value 0.0and density value at first valley 230, between density value at firstvalley 230 and density value at second valley 233, and between densityvalue at second valley 233 and the maximum normalized density value. Inthis example, the maximum normalized density value is 1.0, for example.The computer-implemented method can determine the total number of voxelsbetween density value 0.0 and the first valley 230, between the firstvalley 230 and the second valley 233, and the total number of voxelsbetween the second valley 233. In the example of FIG. 4(a), if thecomputer-implemented method receives information that the dentalimpression type is a triple tray, then the computer-implementeddetermines that the highest voxel count density range corresponds toair. For example, if the density range between 0.0 and the first valley230 contains the highest voxel count density range, then thecomputer-implemented method determines that density range as an airdensity. If the density range between the second valley 233 and themaximum normalized density range value of 1.0 contains the secondhighest voxel count density range for example, then thecomputer-implemented method determines that density range as the objectmaterial (or impression material) density. Similarly, as illustrated inthe example of FIG. 4(b), the computer-implemented method can generate adensity frequency distribution as described previously, thecomputer-implemented method determines voxel count peaks 244, 246, and248 and valleys 240 and 243, thereby establishing density ranges betweendensity value 0.0 and density value at first valley 240, between densityvalue at first valley 240 and density value at second valley 243, andbetween density value at second valley 243 and the maximum normalizeddensity value. In this example, the maximum normalized density value is1.0, for example. The computer-implemented method can determine thetotal number of voxels between density value 0.0 and the first valley240 as total voxel count 249, between the first valley 240 and thesecond valley 243 as total voxel count 242, and the total number ofvoxels after the second valley 243 as total voxel count 250. In thisexample, if the computer-implemented method receives information thatthe dental impression type is a triple tray, then thecomputer-implemented determines that the highest voxel count densityrange will correspond to air. For example, if the total voxel count 249is the highest voxel count density range, then the computer-implementedmethod determines that the density range between 0.0 and the firstvalley 240 as an air density. If the total voxel count 242 is the secondhighest voxel count density range for example, then thecomputer-implemented method determines that the density range betweenthe first valley 240 and the second valley 243 is the object material(or impression material) density. In some embodiments as illustrated inthe examples, the computer-implemented method determines density rangevoxel counts by calculating an area beneath the voxel count curveextending between the particular density range endpoints. For theexample of FIG. 4(a), the computer-implemented method can determine anarea beneath the curve defined by the voxel counts between 0.0 and thedensity value at first valley 230 to determine the total voxel count forthat density range. The computer-implemented method can determine anarea beneath the curve defined by the voxel counts between the densityvalue at first valley 230 and the density value at the second valley 233to determine the total voxel count for that density range. Thecomputer-implemented method can determine an area beneath the curvedefined by the voxel counts between the density value at the secondvalley 233 and the highest density of the normalized density range todetermine the total voxel count for that density range. The same methodsteps can be applied to the example of FIG. 4(b) and of any densityfrequency distribution, for example. In some embodiments, these areasbeneath the curves can be calculated by performing integration on therespective curve with limits corresponding to the density valueendpoints of a particular density range.

In some embodiments, the air and/or material density is determinedautomatically from a histogram per scan.

In some embodiments, the computer-implemented method can subdivide thereconstructed volume into one or more blocks, each block having one ormore voxels. In some embodiments, the blocks can be cubical. In someembodiments, a block size can be a user configurable value. In someembodiments, the block size is 0.5 mm*0.5 mm*0.5 mm, for example. Othersuitable block sizes are contemplated and can be used as well. In someembodiments, the computer-implemented method subdivides the entirereconstructed volume into multiple blocks.

FIG. 5 illustrates an example of reconstructed volume 502. Thereconstructed volume can include one or more voxels representing densityinformation at the location of the voxel in the reconstructed volume. Insome embodiments, the computer-implemented method can in someembodiments divide the entire reconstructed volume into one or morethree dimensional blocks such as block 504. In some embodiments, eachblock is cubical. In some embodiments, all of the blocks have the samedimensions. The number of blocks shown in FIG. 5 is for illustrationpurposes only; the computer-implemented method can generate more orfewer blocks.

Some embodiments of the computer-implemented method can includedetermining one or more contrast blocks within the reconstructed volume.In some embodiments, the determining one or more contrast blocks caninclude determining an air voxel count and a material voxel count ineach of the one or more blocks. For example, in some embodiments, theair voxel count can include the number of voxels having a density belowor equal to the air density. In some embodiments, the material voxelcount can include the number of voxels having a density above or equalto the material density.

FIG. 6(a) illustrates an example of a single block 602 from FIG. 5 , forexample. Each block can contain one or more voxels in some embodiments.In some embodiments, a block can contain no voxels, for example. In someembodiments, the computer-implemented method can determine one or airvoxel counts and material voxel counts. For example, in FIG. 6(a), thesingle block 602 can include a single air density voxel 604. In thisexample, the remaining voxels are material density voxels. If the totalnumber of voxels in the single block 602 is 64, for example, then 63voxels would be material density voxels. In the example, the air voxelcount for the single block 602 would be 1, and the material voxel countwould be 63. In the example of FIG. 6(b), the single block 606 caninclude a first air density voxel 608 and a second air density voxel610. In this example, the remaining voxels are material density voxels.If the total number of voxels in the single block 606 is 64, forexample, then 62 voxels would be material density voxels. In theexample, the air voxel count for the single block 606 would be 2, andthe material voxel count would be 62. In the example of FIG. 6(c), thesingle block 620 can include an air density voxel 622, a first objectmaterial density voxel 628, and a second object material density voxel630. The remaining voxels remaining voxels 624 are neither air norobject material density voxels. If the total number of voxels in thesingle block 620 is 64, for example, then the air voxel count for thesingle block 620 would be 1, and the object material voxel count wouldbe 2, and other material density voxels would be 61.

In some embodiments, a contrast block can include a block where both afraction of air voxels and a fraction of material voxels is greater thana contrast block threshold value of all voxels within the block. In someembodiments, the threshold value can be 2%, as one example. Othersuitable threshold values are contemplated and can be used. In theexample of FIG. 6(a), the fraction of air voxels is 1/64, or 1.6%, andthe fraction of material voxels is 63/64, or 98%. If the threshold valueis set to 2%, for example, then the single block 602 would have airvoxel count below the threshold value and material voxel counts abovethe threshold. In the example, the computer-implemented method woulddetermine that the single block 602 is not a contrast block. In theexample of FIG. 6(b), the fraction of air voxels is 2/64, or 3.1%, andthe fraction of material voxels is 62/64, or 97%. If the threshold valueis set to 2%, for example, then the single block 606 would have airvoxel count above the threshold value and material voxel counts abovethe threshold. In the example, the computer-implemented method woulddetermine that the single block 606 is a contrast block. In the exampleof FIG. 6(c), the fraction of air voxels is 1/64, or 1.6%, and thefraction of object material voxels is 2/64, or 3.1%. If the thresholdvalue is set to 2%, for example, then the single block 620 would haveair voxel count below the threshold value and material voxel countsabove the threshold. In the example, the computer-implemented methodwould determine that the single block 620 is not a contrast block.

Although there are 64 voxels illustrated in the single block 602, thesingle block 606, and the single block 620, more or fewer voxels can bepresent in the blocks. In some embodiments, the computer-implementedmethod can process every block in the reconstructed volume to determinewhether a particular block is a contrast block.

Some embodiments of the computer-implemented method can includedetermining a reconstruction quality based on the number of contrastblocks in the reconstructed volume. In some embodiments, thereconstruction quality determines whether the reconstruction should bekept. In some embodiments, the reconstruction quality is good where thenumber of contrast blocks in the reconstructed volume is greater than areconstruction pass value. In some embodiments, the reconstruction passvalue is a user-configurable value.

In some embodiments, the reconstruction pass value can be stored in auser-configurable file. In some embodiments, the reconstructionthreshold pass value can be 30 blocks, as one example. Other suitablereconstruction threshold pass values are contemplated and can be usedinstead. For example, if the computer-implemented method determines thatat least 30 of the blocks in FIG. 5 are contrast blocks, then thecomputer-implemented method can determine that reconstruction quality ofthe reconstructed volume 502 contains sufficient contrast and thereforeis a good reconstruction, and that the reconstructed volume 502 shouldbe kept. In such a case, the computer-implemented method can allowfurther processing to continue. One example of good reconstructionquality based on contrast blocks can be seen in FIG. 7 , whichillustrates one horizontal slice of a reconstructed volume image 700,for example. In the figure, the dark regions can represent one or moreair voxels, for example, white regions can represent one or more objectmaterial voxels, for example, with the rest belonging to other materialregions. Contrast blocks contain both object material voxels and airvoxels in a user-configurable threshold value. Illustrated in the figureare sample five blocks. These blocks are shown only for reference, anddo not necessarily appear in the reconstructed volume. As can be seen,the reconstructed image can contain contrast blocks such as a firstcontrast block 702, and a second contrast block 704, which cancorrespond to air in some embodiments, for example. The reconstructedimage can also contain non-contrast blocks such as first non-contrastblock 706, second non-contrast block 708, and third non-contrast block710.

In some embodiments, the reconstruction quality is blurry where thenumber of contrast blocks in the reconstructed volume is less than thereconstruction pass value. FIG. 8 illustrates an example of areconstructed volume slice 802 showing the reconstructed volume havingfewer contrast blocks than the reconstruction pass value. As can beseen, the reconstructed image is distorted, blurry or unclear, withlittle distinction between an object and air.

In some embodiments, the computer-implemented method can send an alertupon determining that the number of contrast blocks is not greater thanthe threshold pass value. The alert in some embodiments can be sent asan alarm that can include an audible sound and/or a graphical userinterface window on a display that is part of the computer processingsystem 150 or other computer display. FIG. 9 illustrates one type ofalert as a graphical user interface window 902 along with an alertmessage such as the example message shown in FIG. 9 . Any otheruser-configurable message can be displayed and other types of alertsknown in the art can be issued. For example, in some embodiments,flashing lights can be activated to alert the technician to a problem inthe reconstruction. In some embodiments, the visual alert can becombined with an audible alert. The technician can then adjust eitherthe scan or the calibration parameters of the CT scanner and rescan theobject. This can advantageously help identify issues during automatedscanning, for example. In some embodiments, the blurry reconstructionquality can be due to incorrect calibration parameters. In someembodiments, the reconstruction quality control is performed on a perscan basis.

One or more advantages of one or more features can include for example:a fully automatic process that eliminates manual determination ofreconstruction quality, automatic detection of wrong or outdatedcalibration parameters used for the CT reconstruction, providing awarning/signal in an automated process to change the calibration,providing a warning/signal in the automatic process that a scan isblurry and should be performed again, improved reconstructed imagequality, immediate determination of scanning error, determination ofscanning error per scan, improved quality control at a per scan level,each scan is checked for quality, faster determination of scanningerror, and automatic determination of scanning error.

FIG. 10 illustrates an example in some embodiments of acomputer-implemented method of reconstruction quality control that caninclude receiving a reconstructed volume of an object at 1002,determining an air density and a material density from the reconstructedvolume of the object at 1004, subdividing the reconstructed volume intoone or more blocks, each block can include one or more voxels at 1006,determining one or more contrast blocks within the reconstructed volumeat 1008, and determining a reconstruction quality based on the number ofcontrast blocks in the reconstructed volume at 1010. Some embodimentsinclude a processing system for reconstruction quality control that caninclude a processor, a computer-readable storage medium includinginstructions executable by the processor to perform steps including:receiving a reconstructed volume of an object; determining an airdensity and a material density from the reconstructed volume of theobject; subdividing the reconstructed volume into one or more blocks,each block comprising one or more voxels; determining one or morecontrast blocks within the reconstructed volume; and determining areconstruction quality based on the number of contrast blocks in thereconstructed volume.

Other features in some embodiments of the system, method, orinstructions can also include the reconstructed volume is a CTreconstructed volume. In some embodiments, the object can be a dentalimpression. In some embodiments, the method can further includedetermining an air voxel count and a material voxel count in each of theone or more blocks. In some embodiments, the air voxel count can includethe number of voxels having a density below or equal to the air density.In some embodiments, the material voxel count can include the number ofvoxels having a density above or equal to the material density. In someembodiments, a contrast block can include a block where both a fractionof air voxels and a fraction of material voxels is greater than acontrast block threshold value of all voxels within the block. In someembodiments, the reconstruction quality is good where the number ofcontrast blocks in the reconstructed volume is greater than areconstruction pass value.

FIG. 11 illustrates a processing system 14000 in some embodiments. Thesystem 14000 can include a processor 14030, computer-readable storagemedium 14034 having instructions executable by the processor to performone or more steps described in the present disclosure.

One or more of the features disclosed herein can be performed and/orattained automatically, without manual or user intervention. One or moreof the features disclosed herein can be performed by acomputer-implemented method. The features—including but not limited toany methods and systems—disclosed may be implemented in computingsystems. For example, the computing environment 14042 used to performthese functions can be any of a variety of computing devices (e.g.,desktop computer, laptop computer, server computer, tablet computer,gaming system, mobile device, programmable automation controller, videocard, etc.) that can be incorporated into a computing system comprisingone or more computing devices. In some embodiments, the computing systemmay be a cloud-based computing system.

For example, a computing environment 14042 may include one or moreprocessing units 14030 and memory 14032. The processing units executecomputer-executable instructions. A processing unit 14030 can be acentral processing unit (CPU), a processor in an application-specificintegrated circuit (ASIC), or any other type of processor. In someembodiments, the one or more processing units 14030 can execute multiplecomputer-executable instructions in parallel, for example. In amulti-processing system, multiple processing units executecomputer-executable instructions to increase processing power. Forexample, a representative computing environment may include a centralprocessing unit as well as a graphics processing unit or co-processingunit. The tangible memory 14032 may be volatile memory (e.g., registers,cache, RAM), non-volatile memory (e.g., ROM, EEPROM, flash memory,etc.), or some combination of the two, accessible by the processingunit(s). The memory stores software implementing one or more innovationsdescribed herein, in the form of computer-executable instructionssuitable for execution by the processing unit(s).

A computing system may have additional features. For example, in someembodiments, the computing environment includes storage 14034, one ormore input devices 14036, one or more output devices 14038, and one ormore communication connections 14037. An interconnection mechanism suchas a bus, controller, or network, interconnects the components of thecomputing environment. Typically, operating system software provides anoperating environment for other software executing in the computingenvironment, and coordinates activities of the components of thecomputing environment.

The tangible storage 14034 may be removable or non-removable andincludes magnetic or optical media such as magnetic disks, magnetictapes or cassettes, CD-ROMs, DVDs, or any other medium that can be usedto store information in a non-transitory way and can be accessed withinthe computing environment. The storage 14034 stores instructions for thesoftware implementing one or more innovations described herein.

The input device(s) may be, for example: a touch input device, such as akeyboard, mouse, pen, or trackball; a voice input device; a scanningdevice; any of various sensors; another device that provides input tothe computing environment; or combinations thereof. For video encoding,the input device(s) may be a camera, video card, TV tuner card, orsimilar device that accepts video input in analog or digital form, or aCD-ROM or CD-RW that reads video samples into the computing environment.The output device(s) may be a display, printer, speaker, CD-writer, oranother device that provides output from the computing environment.

The communication connection(s) enable communication over acommunication medium to another computing entity. The communicationmedium conveys information, such as computer-executable instructions,audio or video input or output, or other data in a modulated datasignal. A modulated data signal is a signal that has one or more of itscharacteristics set or changed in such a manner as to encode informationin the signal. By way of example, and not limitation, communicationmedia can use an electrical, optical, RF, or other carrier.

Any of the disclosed methods can be implemented as computer-executableinstructions stored on one or more computer-readable storage media 14034(e.g., one or more optical media discs, volatile memory components (suchas DRAM or SRAM), or nonvolatile memory components (such as flash memoryor hard drives)) and executed on a computer (e.g., any commerciallyavailable computer, including smart phones, other mobile devices thatinclude computing hardware, or programmable automation controllers)(e.g., the computer-executable instructions cause one or more processorsof a computer system to perform the method). The term computer-readablestorage media does not include communication connections, such assignals and carrier waves. Any of the computer-executable instructionsfor implementing the disclosed techniques as well as any data createdand used during implementation of the disclosed embodiments can bestored on one or more computer-readable storage media 14034. Thecomputer-executable instructions can be part of, for example, adedicated software application or a software application that isaccessed or downloaded via a web browser or other software application(such as a remote computing application). Such software can be executed,for example, on a single local computer (e.g., any suitable commerciallyavailable computer) or in a network environment (e.g., via the Internet,a wide-area network, a local-area network, a client-server network (suchas a cloud computing network), or other such network) using one or morenetwork computers.

For clarity, only certain selected aspects of the software-basedimplementations are described. Other details that are well known in theart are omitted. For example, it should be understood that the disclosedtechnology is not limited to any specific computer language or program.For instance, the disclosed technology can be implemented by softwarewritten in C++, Java, Perl, Python, JavaScript, Adobe Flash, or anyother suitable programming language. Likewise, the disclosed technologyis not limited to any particular computer or type of hardware. Certaindetails of suitable computers and hardware are well known and need notbe set forth in detail in this disclosure.

It should also be well understood that any functionality describedherein can be performed, at least in part, by one or more hardware logiccomponents, instead of software. For example, and without limitation,illustrative types of hardware logic components that can be used includeField-programmable Gate Arrays (FPGAs), Program-specific IntegratedCircuits (ASICs), Program-specific Standard Products (ASSPs),System-on-a-chip systems (SOCs), Complex Programmable Logic Devices(CPLDs), etc.

Furthermore, any of the software-based embodiments (comprising, forexample, computer-executable instructions for causing a computer toperform any of the disclosed methods) can be uploaded, downloaded, orremotely accessed through a suitable communication means. Such suitablecommunication means include, for example, the Internet, the World WideWeb, an intranet, software applications, cable (including fiber opticcable), magnetic communications, electromagnetic communications(including RF, microwave, and infrared communications), electroniccommunications, or other such communication means.

In view of the many possible embodiments to which the principles of thedisclosure may be applied, it should be recognized that the illustratedembodiments are only examples and should not be taken as limiting thescope of the disclosure.

What is claimed is:
 1. A computer-implemented method of reconstructionquality control, comprising: receiving a reconstructed volume of anobject; determining an air density and a material density from thereconstructed volume of the object; subdividing the reconstructed volumeinto one or more blocks, each block comprising one or more voxels;determining one or more contrast blocks within the reconstructed volume;and determining a reconstruction quality based on the number of contrastblocks in the reconstructed volume.
 2. The method of claim 1, whereinthe reconstructed volume is a computed tomography (CT) reconstructedvolume.
 3. The method of claim 1, wherein the object is a dentalimpression.
 4. The method of claim 1, further comprising determining anair voxel count and a material voxel count in each of the one or moreblocks.
 5. The method of claim 4, wherein the air voxel count comprisesthe number of voxels having a density below or equal to the air density.6. The method of claim 4, wherein the material voxel count comprises thenumber of voxels having a density above or equal to the materialdensity.
 7. The method of claim 4, wherein a contrast block comprises ablock where both a fraction of air voxels and a fraction of materialvoxels is greater than a contrast block threshold value of all voxelswithin the block.
 8. The method of claim 1, wherein the reconstructionquality is good where the number of contrast blocks in the reconstructedvolume is greater than a reconstruction pass value.
 9. A system ofreconstruction quality control, comprising: a processor; acomputer-readable storage medium comprising instructions executable bythe processor to perform steps comprising: receiving a reconstructedvolume of an object; determining an air density and a material densityfrom the reconstructed volume of the object; subdividing thereconstructed volume into one or more blocks, each block comprising oneor more voxels; determining one or more contrast blocks within thereconstructed volume; and determining a reconstruction quality based onthe number of contrast blocks in the reconstructed volume.
 10. Thesystem of claim 9, further comprising determining an air voxel count anda material voxel count in each of the one or more blocks.
 11. The systemof claim 10, wherein the air voxel count comprises the number of voxelshaving a density below or equal to the air density.
 12. The system ofclaim 10, wherein the material voxel count comprises the number ofvoxels having a density above or equal to the material density.
 13. Thesystem of claim 10, wherein a contrast block comprises a block whereboth a fraction of air voxels and a fraction of material voxels isgreater than a contrast block threshold value of all voxels within theblock.
 14. The system of claim 9, wherein the reconstruction quality isgood where the number of contrast blocks in the reconstructed volume isgreater than a reconstruction pass value.
 15. A non-transitory computerreadable medium storing executable computer program instructions forreconstruction quality control, the computer program instructionscomprising instructions for: receiving a reconstructed volume of anobject; determining an air density and a material density from thereconstructed volume of the object; subdividing the reconstructed volumeinto one or more blocks, each block comprising one or more voxels;determining one or more contrast blocks within the reconstructed volume;and determining a reconstruction quality based on the number of contrastblocks in the reconstructed volume.
 16. The medium of claim 15, furthercomprising determining an air voxel count and a material voxel count ineach of the one or more blocks.
 17. The medium of claim 16, wherein theair voxel count comprises the number of voxels having a density below orequal to the air density.
 18. The medium of claim 16, wherein thematerial voxel count comprises the number of voxels having a densityabove or equal to the material density.
 19. The medium of claim 16,wherein a contrast block comprises a block where both a fraction of airvoxels and a fraction of material voxels is greater than a contrastblock threshold value of all voxels within the block.
 20. The medium ofclaim 15, wherein the reconstruction quality is good where the number ofcontrast blocks in the reconstructed volume is greater than areconstruction pass value.